Hybrid Cooperative Positioning for Vehicular Networks

Xiong, Jun; Cheong, Joon Wayn; Xiong, Zhi; Dempster, Andrew G.; Tian, Shengfeng; Wang, Rong

doi:10.1109/tvt.2019.2953687

Cited by 23 publications

(12 citation statements)

References 49 publications

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“…The 3D state of those vehicles consist of 2D location and heading angle, the 2D control vector is constituted by linear and angular velocity, while the 3D measurement vector consist of GPS measurements and IMU heading measurement. At the linear measurement model, the identity matrix I 3 is multiplied by the state, as in (8). The main steps of the proposed CCEKF are summarized on Algorithm 1, based on EKF algorithm of [11].…”

Section: A Centralized Trackingmentioning

confidence: 99%

“…The work of [7], presents a robust cubature Kalman Filter (KF) enhanced by Huber M-estimation, in order to improve the performance of the data fusion under the presence of measurement outliers, though not considering the significant role which plays the VANET's size in location estimation. In [8], a Bayesian method is proposed, based on distributed generalized message passing and KF, which utilizes measurements from Inertial Measurement Unit (IMU), GPS, Signals of Opportunity and range measurements using V2V and V2I. A distributed non-Bayesian method is presented in [12], which fuses absolute positions, relative distances and angles of only four vehicles, using the maximum likelihood estimator (MLE).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Graph Laplacian Extended Kalman Filter for Connected and Automated Vehicles Localization

Piperigkos

Lalos

Berberidis

2021

2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)

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Extended Kalman Filters have been widely applied for tracking the location of moving semi-autonomous vehicles. The latter are equipped with a multitude of sensors generating multi-modal data, while at the same time they are capable of cooperating via Vehicle-to-Vehicle communication technologies. In this paper, we have formulated a cooperative tracking scheme based on Extended Kalman Filter, in order to cope with erroneous GPS location information. It performs multi-modal fusion in a centralized and distributed manner, assuming the existence of an overall fusion center or local interaction among neighbouring and connected vehicles only. It features the property of encoding in a linear form the different measurement modalities, including range and GPS measurements, exploiting the connectivity topology of cooperating vehicles, using the graph Laplacian operator. The extended experimental evaluation using realistic vehicle trajectories extracted by CARLA autonomous driving simulator, verify the significant reduction of GPS error under various realistic conditions. Moreover, both schemes outperform existing cooperative localization methods. Finally, the distributed tracking approach exhibits similar performance and in specific cases outperforms the centralized counterpart.

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Section: A Centralized Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Graph Laplacian Extended Kalman Filter for Connected and Automated Vehicles Localization

Piperigkos

Lalos

Berberidis

2021

2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)

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show abstract

“…And, when two satellites i and j are both available to vehicles A and B , the receiver clock errors can be further removed by double-difference technique. 18,19 The double-differenced pseudoranges for vehicles A and B and satellites i and j can be defined as…”

Section: Fault-tolerant Relative Navigationmentioning

confidence: 99%

Fault-tolerant relative navigation based on Kullback–Leibler divergence

Xiong

Cheong

Xiong

et al. 2020

International Journal of Advanced Robotic Systems

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A fault-detection method for relative navigation based on Kullback–Leibler divergence (KLD) is proposed. Different from the traditional χ 2-based approaches, the KLD for a filter is following a hybrid distribution that combines χ 2 distribution and F-distribution. Using extended Kalman filter (EKF) as the estimator, the distance between the priori and posteriori data of EKF is calculated to detect the abnormal measurements. After fault detection step, a fault exclusion method is applied to remove the error observations from the fusion procedure. The proposed method is suitable for the Kalman filter-based multisensor relative navigation system. Simulation and experimental results show that the proposed method can detect the abnormal measurement successfully, and its positioning accuracy after fault detection and exclusion outperforms the traditional χ 2-based method.

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“…3 The main defect of these solutions is that the positioning accuracy of the satellite navigation system will be degraded with the reduction in the number of visible satellites when working in some restricted scenarios. 4 Therefore, in some scenarios with high positioning accuracy requirements, ordinary GNSS navigation systems are difficult to meet the needs. By enhancing the positioning performance of UAV swarm through a certain positioning base station and external measurements such as Lidar, cameras and opportunity signals, the real-time kinematic (RTK) technique provides effective ways to solve the navigation problem with UAVs, 5–7 but such applications are limited by strict working scenarios and costs.…”

Section: Introductionmentioning

confidence: 99%

A hybrid cooperative navigation method for UAV swarm based on factor graph and Kalman filter

Chen

Xiong

2022

International Journal of Distributed Sensor Networks

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Navigation plays an important role in the task execution of the micro-unmanned aerial vehicle (UAV) swarm. The Cooperative Navigation (CN) method that fuses the observation of onboard sensors and relative information between UAVs is a research hotspot. Aiming at the efficiency and accuracy problems of previous studies, this article proposes a hybrid-CN method for UAV swarm based on Factor Graph and Kalman filter. A global Factor Graph is used to combine Global Navigation Satellite System (GNSS) and ranging information to provide position estimations for modifying the distributed Kalman filter; distributed Kalman filter is established on each UAV to fuse inertial information and optimized position estimation to modify the navigation states. In order to provide time-consistent GNSS position information for the Factor Graph, a time synchronization filter is designed. The proposed method is tested and verified using standard Monte Carlo simulations, simulation results show that it can provide a more precise and efficient CN solution than traditional CN methods.

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Hybrid Cooperative Positioning for Vehicular Networks

Cited by 23 publications

References 49 publications

Graph Laplacian Extended Kalman Filter for Connected and Automated Vehicles Localization

Graph Laplacian Extended Kalman Filter for Connected and Automated Vehicles Localization

Fault-tolerant relative navigation based on Kullback–Leibler divergence

A hybrid cooperative navigation method for UAV swarm based on factor graph and Kalman filter

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